{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node classification with Cluster-GCN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/cluster-gcn-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/cluster-gcn-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to use `StellarGraph`'s implementation of *Cluster-GCN*, [1], for node classification on a homogeneous graph.\n",
    "\n",
    "*Cluster-GCN* is a training method for scalable training of deeper Graph Neural Networks using Stochastic Gradient Descent (SGD). It is implemented as the `ClusterNodeGenerator` class ([docs](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.mapper.ClusterNodeGenerator)) in StellarGraph, which can be used with [GCN](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GCN) [2] (demonstrated here), [GAT](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GAT) and [APPNP](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.APPNP) models.\n",
    "\n",
    "As a first step, *Cluster-GCN* splits a given graph into `k` non-overlapping subgraphs, i.e., no two subgraphs share a node. In [1], it is suggested that for best classification performance, the *METIS* graph clustering algorithm, [3], should be utilised; *METIS* groups together nodes that form a well connected neighborhood with few connections to other subgraphs. The default clustering algorithm `StellarGraph` uses is the random assignment of nodes to clusters. The user is free to use any suitable clustering algorithm to determine the clusters before training the *Cluster-GCN* model. \n",
    "\n",
    "This notebook demonstrates how to use either random clustering or METIS. For the latter, it is necessary that 3rd party software has correctly been installed; later, we provide links to websites that host the software and provide detailed installation instructions. \n",
    "\n",
    "During model training, each subgraph or combination of subgraphs is treated as a mini-batch for estimating the parameters of a *GCN* model. A pass over all subgraphs is considered a training epoch.\n",
    "\n",
    "*Cluster-GCN* further extends *GCN* from the transductive to the inductive setting but this is not demonstrated in this notebook.\n",
    "\n",
    "This notebook demonstrates *Cluster-GCN* for node classification using 2 citation network datasets, `Cora` and `PubMed-Diabetes`.\n",
    "\n",
    "**References**\n",
    "\n",
    "[1] Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. W. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C. Hsiej, KDD, 2019, arXiv:1905.07953 ([download link](https://arxiv.org/abs/1905.07953))\n",
    "\n",
    "[2] Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling. ICLR 2017. arXiv:1609.02907 ([download link](https://arxiv.org/abs/1609.02907))\n",
    "\n",
    "[3] METIS: Serial Graph Partitioning and Fill-reducing Matrix Ordering. ([download link](http://glaros.dtc.umn.edu/gkhome/views/metis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import itertools\n",
    "import json\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from networkx.readwrite import json_graph\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import ClusterNodeGenerator\n",
    "from stellargraph.layer import GCN\n",
    "from stellargraph import globalvar\n",
    "\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the dataset\n",
    "\n",
    "This notebook demonstrates node classification using the *Cluster-GCN* algorithm using one of two citation networks, `Cora` and `Pubmed`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "The PubMed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(datasets.Cora().description))\n",
    "display(HTML(datasets.PubMedDiabetes().description))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [],
   "source": [
    "dataset = \"cora\"  # can also select 'pubmed'\n",
    "\n",
    "if dataset == \"cora\":\n",
    "    G, labels = datasets.Cora().load()\n",
    "elif dataset == \"pubmed\":\n",
    "    G, labels = datasets.PubMedDiabetes().load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5429\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Features: float32 vector, length 1433\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5429]\n",
      "        Weights: all 1 (default)\n",
      "        Features: none\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the **subject** or **label** (depending on the dataset) attribute on the nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "The number of labeled nodes we use for training depends on the dataset. We use 140 labeled nodes for `Cora` and 60 for `Pubmed` training. The validation and test sets have the same sizes for both datasets. We use 500 nodes for validation and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if dataset == \"cora\":\n",
    "    train_size = 140\n",
    "elif dataset == \"pubmed\":\n",
    "    train_size = 60\n",
    "\n",
    "train_labels, test_labels = model_selection.train_test_split(\n",
    "    labels, train_size=train_size, test_size=None, stratify=labels\n",
    ")\n",
    "val_labels, test_labels = model_selection.train_test_split(\n",
    "    test_labels, train_size=500, test_size=None, stratify=test_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Theory': 18,\n",
       "         'Genetic_Algorithms': 22,\n",
       "         'Probabilistic_Methods': 22,\n",
       "         'Reinforcement_Learning': 11,\n",
       "         'Neural_Networks': 42,\n",
       "         'Case_Based': 16,\n",
       "         'Rule_Learning': 9})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = preprocessing.LabelBinarizer()\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_labels)\n",
    "val_targets = target_encoding.transform(val_labels)\n",
    "test_targets = target_encoding.transform(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we prepare a Pandas DataFrame holding the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. `Cora` contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero. `Pubmed` has similar feature vectors associated with each node but the values are [tf-idf.](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train using cluster GCN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph Clustering \n",
    "\n",
    "*Cluster-GCN* requires that a graph is clustered into `k` non-overlapping subgraphs. These subgraphs are used as batches to train a *GCN* model. \n",
    "\n",
    "Any graph clustering method can be used, including random clustering that is the default clustering method in `StellarGraph`. \n",
    "\n",
    "However, the choice of clustering algorithm can have a large impact on performance. In the *Cluster-GCN* paper, [1], it is suggested that the *METIS* algorithm is used as it produces subgraphs that are well connected with few intra-graph edges. \n",
    "\n",
    "This demo uses random clustering by default. \n",
    "\n",
    "#### METIS\n",
    "\n",
    "In order to use *METIS*, you must download and install the official implementation from [here](http://glaros.dtc.umn.edu/gkhome/views/metis). Also, you must install the Python `metis` library by following the instructions [here.](https://metis.readthedocs.io/en/latest/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_clusters = 10  # the number of clusters/subgraphs\n",
    "clusters_per_batch = 2  # combine two cluster per batch\n",
    "random_clusters = True  # Set to False if you want to use METIS for clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_ids = np.array(G.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if random_clusters:\n",
    "    # We don't have to specify the cluster because the CluserNodeGenerator will take\n",
    "    # care of the random clustering for us.\n",
    "    clusters = number_of_clusters\n",
    "else:\n",
    "    # We are going to use the METIS clustering algorith,\n",
    "    print(\"Graph clustering using the METIS algorithm.\")\n",
    "\n",
    "    import metis\n",
    "\n",
    "    lil_adj = G.to_adjacency_matrix().tolil()\n",
    "    adjlist = [tuple(neighbours) for neighbours in lil_adj.rows]\n",
    "\n",
    "    edgecuts, parts = metis.part_graph(adjlist, number_of_clusters)\n",
    "    parts = np.array(parts)\n",
    "    clusters = []\n",
    "    cluster_ids = np.unique(parts)\n",
    "    for cluster_id in cluster_ids:\n",
    "        mask = np.where(parts == cluster_id)\n",
    "        clusters.append(node_ids[mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create the `ClusterNodeGenerator` object ([docs](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.mapper.ClusterNodeGenerator)) that will give us access to a generator suitable for model training, evaluation, and prediction via the Keras API. \n",
    "\n",
    "We specify the number of clusters and the number of clusters to combine per batch, **q**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters 10\n",
      "0 cluster has size 270\n",
      "1 cluster has size 270\n",
      "2 cluster has size 270\n",
      "3 cluster has size 270\n",
      "4 cluster has size 270\n",
      "5 cluster has size 270\n",
      "6 cluster has size 270\n",
      "7 cluster has size 270\n",
      "8 cluster has size 270\n",
      "9 cluster has size 278\n"
     ]
    }
   ],
   "source": [
    "generator = ClusterNodeGenerator(G, clusters=clusters, q=clusters_per_batch, lam=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use two GCN layers with 32-dimensional hidden node features at each layer.\n",
    " * `activations` is a list of activations applied to each layer's output\n",
    " * `dropout=0.5` specifies a 50% dropout at each layer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the `GCN` model ([docs](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GCN)) for training with *Cluster-GCN* as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cluster_gcn = GCN(\n",
    "    layer_sizes=[32, 32], activations=[\"relu\", \"relu\"], generator=generator, dropout=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a Keras model we now expose the input and output tensors of the *Cluster-GCN* model for node prediction, via the `GCN.in_out_tensors` method ([docs](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GCN.in_out_tensors)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = cluster_gcn.in_out_tensors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'input_1:0' shape=(1, None, 1433) dtype=float32>,\n",
       " <tf.Tensor 'input_2:0' shape=(1, None) dtype=int32>,\n",
       " <tf.Tensor 'input_3:0' shape=(1, None, None) dtype=float32>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'gather_indices/Identity:0' shape=(1, None, 32) dtype=float32>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also going to add a final layer dense layer with softmax output activation. This layers performs classification so we set the number of units to equal the number of classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'dense/Identity:0' shape=(1, None, 7) dtype=float32>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we build the TensorFlow model and compile it specifying the loss function, optimiser, and metrics to monitor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=predictions)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.01),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to train the `GCN` model using *Cluster-GCN*, keeping track of its loss and accuracy on the training set, and its generalisation performance on a validation set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need two generators, one for training and one for validation data. We can create such generators by calling the `flow` method of the `ClusterNodeGenerator` object we created earlier and specifying the node IDs and corresponding ground truth target values for each of the two datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_labels.index, train_targets, name=\"train\")\n",
    "val_gen = generator.flow(val_labels.index, val_targets, name=\"val\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we are ready to train our `GCN` model by calling the `fit` method of our TensorFlow Keras model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "5/5 [==============================] - 0s 58ms/step - loss: 1.9268 - acc: 0.2214 - val_loss: 1.8233 - val_acc: 0.3080\n",
      "Epoch 2/20\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 1.7361 - acc: 0.3000 - val_loss: 1.7150 - val_acc: 0.3020\n",
      "Epoch 3/20\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 1.5788 - acc: 0.3286 - val_loss: 1.6156 - val_acc: 0.3460\n",
      "Epoch 4/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 1.3568 - acc: 0.4929 - val_loss: 1.4368 - val_acc: 0.5460\n",
      "Epoch 5/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 1.1250 - acc: 0.6643 - val_loss: 1.2956 - val_acc: 0.5900\n",
      "Epoch 6/20\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 0.8347 - acc: 0.7500 - val_loss: 1.2476 - val_acc: 0.5940\n",
      "Epoch 7/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.7765 - acc: 0.7714 - val_loss: 1.1848 - val_acc: 0.6000\n",
      "Epoch 8/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.5123 - acc: 0.8786 - val_loss: 1.1179 - val_acc: 0.6360\n",
      "Epoch 9/20\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 0.3948 - acc: 0.8857 - val_loss: 1.0722 - val_acc: 0.6380\n",
      "Epoch 10/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.4239 - acc: 0.8857 - val_loss: 1.2471 - val_acc: 0.6180\n",
      "Epoch 11/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.3772 - acc: 0.8429 - val_loss: 1.3767 - val_acc: 0.6280\n",
      "Epoch 12/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.3261 - acc: 0.8857 - val_loss: 1.3026 - val_acc: 0.6220\n",
      "Epoch 13/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.2840 - acc: 0.9286 - val_loss: 1.2333 - val_acc: 0.6240\n",
      "Epoch 14/20\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 0.2482 - acc: 0.9214 - val_loss: 1.3059 - val_acc: 0.6240\n",
      "Epoch 15/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.3782 - acc: 0.8786 - val_loss: 1.3824 - val_acc: 0.6080\n",
      "Epoch 16/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.2377 - acc: 0.9143 - val_loss: 1.3908 - val_acc: 0.5820\n",
      "Epoch 17/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.2001 - acc: 0.9143 - val_loss: 1.4153 - val_acc: 0.6020\n",
      "Epoch 18/20\n",
      "5/5 [==============================] - 0s 22ms/step - loss: 0.3026 - acc: 0.9286 - val_loss: 1.3616 - val_acc: 0.6220\n",
      "Epoch 19/20\n",
      "5/5 [==============================] - 0s 23ms/step - loss: 0.2449 - acc: 0.9286 - val_loss: 1.2396 - val_acc: 0.6440\n",
      "Epoch 20/20\n",
      "5/5 [==============================] - 0s 24ms/step - loss: 0.1909 - acc: 0.9357 - val_loss: 1.3197 - val_acc: 0.6420\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_gen, validation_data=val_gen, epochs=20, verbose=1, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the training history:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.utils.plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the best model on the test set.\n",
    "\n",
    "Note that *Cluster-GCN* performance can be very poor if using random graph clustering. Using *METIS* instead of random graph clustering produces considerably better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_labels.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 6ms/step - loss: 1.2021 - acc: 0.6833\n",
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 1.2021\n",
      "\tacc: 0.6833\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For predictions to work correctly, we need to remove the extra batch dimensions necessary for the implementation of *Cluster-GCN* to work. We can easily achieve this by adding a layer after the dense predictions layer to remove this extra dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_flat = layers.Lambda(lambda x: K.squeeze(x, 0))(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor 'dense/Identity:0' shape=(1, None, 7) dtype=float32>,\n",
       " <tf.Tensor 'lambda_1/Identity:0' shape=(None, 7) dtype=float32>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice that we have removed the first dimension\n",
    "predictions, predictions_flat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions for all nodes.\n",
    "\n",
    "We need to create a new model using the same as before input Tensor and our new **predictions_flat** Tensor as the output. We are going to re-use the trained model weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_predict = Model(inputs=x_inp, outputs=predictions_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = list(G.nodes())\n",
    "all_gen = generator.flow(all_nodes, name=\"all_gen\")\n",
    "all_predictions = model_predict.predict(all_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708, 7)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specification to turn these values back to the original categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few predictions after training the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2708"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_gen.node_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Theory</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>Theory</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>Rule_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Predicted                    True\n",
       "35       Genetic_Algorithms      Genetic_Algorithms\n",
       "40       Genetic_Algorithms      Genetic_Algorithms\n",
       "114  Reinforcement_Learning  Reinforcement_Learning\n",
       "117  Reinforcement_Learning  Reinforcement_Learning\n",
       "128  Reinforcement_Learning  Reinforcement_Learning\n",
       "130  Reinforcement_Learning  Reinforcement_Learning\n",
       "164                  Theory                  Theory\n",
       "288                  Theory  Reinforcement_Learning\n",
       "424                  Theory           Rule_Learning\n",
       "434           Rule_Learning  Reinforcement_Learning"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.Series(node_predictions, index=all_gen.node_order)\n",
    "df = pd.DataFrame({\"Predicted\": results, \"True\": labels})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "\n",
    "Evaluate node embeddings as activations of the output of the last graph convolution layer in the `GCN` layer stack and visualise them, coloring nodes by their true subject label. We expect to see nice clusters of papers in the node embedding space, with papers of the same subject belonging to the same cluster.\n",
    "\n",
    "To calculate the node embeddings rather than the class predictions, we create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings `x_out` rather than the predicted class. Additionally note that the weights trained previously are kept in the new model.\n",
    "\n",
    "Note that the embeddings from the `GCN` model have a batch dimension of 1 so we `squeeze` this to get a matrix of $N_{nodes} \\times N_{emb}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_out_flat = layers.Lambda(lambda x: K.squeeze(x, 0))(x_out)\n",
    "embedding_model = Model(inputs=x_inp, outputs=x_out_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 5ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2708, 32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict(all_gen, verbose=1)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their true subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction Node Order**\n",
    "\n",
    "The predictions are not returned in the same order as the input nodes given. The generator object internally maintains the order of predictions. These are stored in the object's member variable `node_order`. We use `node_order` to re-index the `node_data` DataFrame such that the prediction order in `y` corresponds to that of node embeddings in `X`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(labels.reindex(index=all_gen.node_order)), axis=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # or use PCA for speed\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=all_gen.node_order)\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=list(G.nodes()))\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GCN embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/cluster-gcn-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/cluster-gcn-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
